import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.stats.outliers_influence import variance_inflation_factor
from sklearn.model_selection import train_test_split
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import KFold, cross_val_score
from sklearn import metrics
from sklearn.metrics import roc_curve
from sklearn.model_selection import cross_val_predict
from sklearn.linear_model import LogisticRegression
pd.set_option('display.max_columns', None)
log_reg = LogisticRegression(random_state=42, max_iter=10000, penalty='l2')
# log_reg.fit(X_train, y_train)
